brain_guide_pipeline.py

import inspect
import math
from typing import Any, Callable, Dict, List, Optional, Union

import torch
import torch.nn.functional as F
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNet2DConditionModel
import matplotlib.pyplot as plt
import gc
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
    deprecate,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
import numpy as np
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline
from torchvision import transforms
from diffusers import LMSDiscreteScheduler, PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler

from diffusers.models import AutoencoderKL, UNet2DConditionModel

import random


# def shuffle_shift(input_image, extent=2, seed=0):
#     random.seed(seed)
#     offset_x = random.randint(-extent, extent)
#     offset_y = random.randint(-extent, extent)
#     orig_shape = input_image.shape
#     temp = input_image[:, :, max(0, offset_x):min(orig_shape[2], orig_shape[2] + offset_x),
#            max(0, offset_y):min(orig_shape[3], orig_shape[3] + offset_y)]
#     #     temp = torch.nn.functional.pad(temp, (max(0,offset_y), max(0, -offset_y), max(0,offset_x), max(0, -offset_x)), mode='replicate')
#     temp_out = torch.nn.functional.pad(temp, (max(0, -offset_y), max(0, offset_y), max(0, -offset_x), max(0, offset_x)),
#                                        mode='replicate')
#     return temp_out


# processes and stores attention probabilities
class CrossAttnStoreProcessor:
    def __init__(self):
        self.attention_probs = None

    def __call__(
            self,
            attn,
            hidden_states,
            encoder_hidden_states=None,
            attention_mask=None,
    ):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.cross_attention_norm:
            encoder_hidden_states = attn.norm_cross(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)
        self.attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(self.attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import StableDiffusionPipeline

        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        >>> pipe = pipe.to("cuda")

        >>> prompt = "a photo of an astronaut riding a horse on mars"
        >>> image = pipe(prompt).images[0]
        ```
"""


def blur_grad(input_grad):
    mygrad = input_grad.detach().cpu().numpy()[0]
    mygrad = np.moveaxis(mygrad, (0, 1, 2), (2, 0, 1))
    mygrad = mygrad - np.min(mygrad)
    mygrad = mygrad / np.max(mygrad)
    plt.imshow(mygrad)
    plt.show()
    return input_grad
    return torchvision.transforms.GaussianBlur(kernel_size=9, sigma=0.2)(input_grad)


# Modified to get self-attention guidance scale in this paper (https://arxiv.org/pdf/2210.00939.pdf) as an input
class mypipelineSAG(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
            self,
            vae: AutoencoderKL,
            text_encoder: CLIPTextModel,
            tokenizer: CLIPTokenizer,
            unet: UNet2DConditionModel,
            scheduler: KarrasDiffusionSchedulers,
            safety_checker: StableDiffusionSafetyChecker,
            feature_extractor: CLIPFeatureExtractor,
            requires_safety_checker: bool = True,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    def remove_unused_weights(self):
        self.register_modules(feature_extractor=None)
        self.register_modules(safety_checker=None)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding.

        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
        steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
    def enable_sequential_cpu_offload(self, gpu_id=0):
        r"""
        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
        Note that offloading happens on a submodule basis. Memory savings are higher than with
        `enable_model_cpu_offload`, but performance is lower.
        """
        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
            from accelerate import cpu_offload
        else:
            raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")

        device = torch.device(f"cuda:{gpu_id}")

        if self.device.type != "cpu":
            self.to("cpu", silence_dtype_warnings=True)
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
            cpu_offload(cpu_offloaded_model, device)

        if self.safety_checker is not None:
            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)

    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def _execution_device(self):
        r"""
        Returns the device on which the pipeline's models will be executed. After calling
        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
        hooks.
        """
        if not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                    hasattr(module, "_hf_hook")
                    and hasattr(module._hf_hook, "execution_device")
                    and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
    def _encode_prompt(
            self,
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt=None,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
        """
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1: -1]
                )
            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
            )
        else:
            has_nsfw_concept = None
        return image, has_nsfw_concept

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
    def decode_latents(self, latents):
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents).sample
        image = (image / 2 + 0.5).clamp(0, 1)
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
    def check_inputs(
            self,
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt=None,
            prompt_embeds=None,
            negative_prompt_embeds=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if (callback_steps is None) or (
                callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    @torch.enable_grad()
    def cond_fn(self,
                latents,
                timestep,
                index,
                text_embeddings,
                noise_pred_original,
                clip_guidance_scale):
        latents = latents.detach().requires_grad_()
        latent_model_input = self.scheduler.scale_model_input(latents, timestep)

        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample

        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
            beta_prod_t = 1 - alpha_prod_t
            # compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
            fac = torch.sqrt(beta_prod_t)
            sample = pred_original_sample * (fac) + latents * (1 - fac)
        #             sample = pred_original_sample
        #             image = self.decode_latents(pred_original_sample.detach())
        #             image2 = self.decode_latents(latents.detach())
        #             image3 = self.decode_latents(sample.detach())
        #             print(image.shape, "IMAGE SHAPE", fac)
        #             image = np.concatenate([image, image2, image3], axis=2)
        #             image = self.numpy_to_pil(image)[0]
        #             plt.imshow(image)
        #             plt.title(timestep)
        #             plt.show()
        #         return noise_pred_original, latents
        elif isinstance(self.scheduler, LMSDiscreteScheduler):
            sigma = self.scheduler.sigmas[index]
            sample = latents - sigma * noise_pred
        else:
            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")

        sample = 1 / self.vae.config.scaling_factor * sample
        image = self.vae.decode(sample).sample
        image = (image / 2 + 0.5).clamp(0, 1)
        if not hasattr(self, "OPENAI_CLIP_MEAN"):
            self.OPENAI_CLIP_MEAN = torch.from_numpy(
                np.array((0.48145466, 0.4578275, 0.40821073), dtype=np.single)[None, :, None, None]).to(image.device)
            self.OPENAI_CLIP_STD = torch.from_numpy(
                np.array((0.26862954, 0.26130258, 0.27577711), dtype=np.single)[None, :, None, None]).to(image.device)
            self.OPENAI_CLIP_MEAN.requires_grad = False
            self.OPENAI_CLIP_STD.requires_grad = False
        fp32_image = image.float()
        resized_image = torch.nn.functional.interpolate(fp32_image, size=224, mode="bilinear")
        normalized_image = (resized_image - self.OPENAI_CLIP_MEAN) / self.OPENAI_CLIP_STD
        loss = self.brain_tweak(normalized_image) * clip_guidance_scale
        grads = -torch.autograd.grad(loss, latents)[0]
        del loss
        del normalized_image
        del resized_image
        del fp32_image
        del image

        latents = latents.detach()
        if isinstance(self.scheduler, LMSDiscreteScheduler):
            latents = latents.detach() + grads * (sigma ** 2)
            noise_pred = noise_pred_original
        else:
            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
            # noise_pred_mimic = noise_pred_original - torch.sqrt(beta_prod_t) * grads_small
            # noise_pred = clamp_latent(noise_pred, noise_pred_mimic, percentile=0.99)

            # print(torch.max(torch.abs(noise_pred_original)), torch.max(torch.abs(torch.sqrt(beta_prod_t) * grads)), "GRAD")
            # print((torch.std(noise_pred_original)), (torch.std(torch.sqrt(beta_prod_t) * grads)), "GRAD")
        #             noise_pred = noise_pred_original - grads*0.25
        return noise_pred, latents

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    @replace_example_docstring(EXAMPLE_DOC_STRING)
    @torch.no_grad()
    def __call__(
            self,
            prompt: Union[str, List[str]] = None,
            height: Optional[int] = None,
            width: Optional[int] = None,
            num_inference_steps: int = 50,
            guidance_scale: float = 7.5,
            sag_scale: float = 0.75,
            clip_guidance_scale=100.0,
            negative_prompt: Optional[Union[str, List[str]]] = None,
            num_images_per_prompt: Optional[int] = 1,
            eta: float = 0.0,
            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
            latents: Optional[torch.FloatTensor] = None,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
            output_type: Optional[str] = "pil",
            return_dict: bool = True,
            callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
            callback_steps: Optional[int] = 1,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            sag_scale (`float`, *optional*, defaults to 0.75):
                SAG scale as defined in [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance]
                (https://arxiv.org/abs/2210.00939). `sag_scale` is defined as `s_s` of equation (24) of SAG paper:
                https://arxiv.org/pdf/2210.00939.pdf. Typically chosen between [0, 1.0] for better quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
                `self.processor` in
                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
        )
        self.grad_diff = 0.0

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0
        # and `sag_scale` is` `s` of equation (15)
        # of the self-attentnion guidance paper: https://arxiv.org/pdf/2210.00939.pdf
        # `sag_scale = 0` means no self-attention guidance
        do_self_attention_guidance = sag_scale > 0.0

        # 3. Encode input prompt
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )
        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        store_processor = CrossAttnStoreProcessor()
        self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        # with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input,
                t,
                encoder_hidden_states=prompt_embeds,
                cross_attention_kwargs=cross_attention_kwargs,
            ).sample
            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                #                     if i == 0:
                #                         print(torch.flatten(noise_pred_uncond)[:3], torch.flatten(noise_pred_text)[:3], "uncond noises")
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # noise_pred = clamp_latent(noise_pred)
                # print(noise_pred.shape, "NOISE SHAPE")
            #                 if i ==0:
            #                     print(torch.flatten(latents)[:3], "LATENT")
            # perform self-attention guidance with the stored self-attentnion map
            if do_self_attention_guidance:
                # classifier-free guidance produces two chunks of attention map
                # and we only use unconditional one according to equation (24)
                # in https://arxiv.org/pdf/2210.00939.pdf
                if do_classifier_free_guidance:
                    # DDIM-like prediction of x0
                    pred_x0 = self.pred_x0(latents, noise_pred_uncond, t)
                    #                         if i == 0:
                    #                             print(torch.flatten(latents)[:3], torch.flatten(noise_pred_uncond)[:3], "CLASSIFIER")
                    #                         if i == 0:
                    #                             print(torch.flatten(pred_x0)[:3], "GUIDED")
                    # get the stored attention maps
                    uncond_attn, cond_attn = store_processor.attention_probs.chunk(2)
                    # self-attention-based degrading of latents
                    #                         for zzz in [pred_x0, uncond_attn, t, self.pred_epsilon(latents, noise_pred_uncond, t)]:
                    #                             print(zzz.shape, "SHAPE")
                    #                         print(pred_x0.shape, uncond_attn, t, self.pred_epsilon(latents, noise_pred_uncond, t))
                    degraded_latents = self.sag_masking(
                        pred_x0, uncond_attn, t, self.pred_epsilon(latents, noise_pred_uncond, t)
                    )
                    uncond_emb, _ = prompt_embeds.chunk(2)
                    # forward and give guidance
                    degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=uncond_emb).sample
                    noise_pred += sag_scale * (noise_pred_uncond - degraded_pred)
                else:
                    # DDIM-like prediction of x0
                    pred_x0 = self.pred_x0(latents, noise_pred, t)
                    #                         if i == 0:
                    #                             print(torch.flatten(latents)[:3], torch.flatten(noise_pred)[:3], "RAW UNCOND")

                    # get the stored attention maps
                    cond_attn = store_processor.attention_probs
                    # self-attention-based degrading of latents
                    degraded_latents = self.sag_masking(
                        pred_x0, cond_attn, t, self.pred_epsilon(latents, noise_pred, t)
                    )
                    # forward and give guidance
                    degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=prompt_embeds).sample
                    noise_pred += sag_scale * (noise_pred - degraded_pred)
            if clip_guidance_scale > 0:
                #                     self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor.save = False
                text_embeddings_for_guidance = (
                    prompt_embeds.chunk(2)[1] if do_classifier_free_guidance else prompt_embeds
                )
                noise_pred, latents = self.cond_fn(
                    latents,
                    t,
                    i,
                    text_embeddings_for_guidance,
                    noise_pred,
                    clip_guidance_scale
                )
            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
            # print(latents.shape, "LATENTS SHAPE")
            # old_latents_uncond = latents[0]+0.0
            # latents = clamp_latent(latents)
            # latents[0] = old_latents_uncond

            # call the callback, if provided
            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                # progress_bar.update()
                if callback is not None and i % callback_steps == 0:
                    callback(i, t, latents)

        # 8. Post-processing
        image = self.decode_latents(latents)

        # 9. Run safety checker
        image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

        # 10. Convert to PIL
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

    def sag_masking(self, original_latents, attn_map, t, eps):
        # Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf
        bh, hw1, hw2 = attn_map.shape
        b, latent_channel, latent_h, latent_w = original_latents.shape
        h = self.unet.attention_head_dim
        if isinstance(h, list):
            h = h[-1]
        map_size = math.isqrt(hw1)

        # Produce attention mask
        attn_map = attn_map.reshape(b, h, hw1, hw2)
        attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0
        attn_mask = (
            attn_mask.reshape(b, map_size, map_size).unsqueeze(1).repeat(1, latent_channel, 1, 1).type(attn_map.dtype)
        )
        attn_mask = F.interpolate(attn_mask, (latent_h, latent_w))

        # Blur according to the self-attention mask
        degraded_latents = self.gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0)
        degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask)

        # Noise it again to match the noise level
        degraded_latents = self.scheduler.add_noise(degraded_latents, noise=eps, timesteps=t)

        return degraded_latents

    # Modified from diffusers.schedulers.scheduling_ddim.DDIMScheduler.step
    # Note: there are some schedulers that clip or do not return x_0 (PNDMScheduler, DDIMScheduler, etc.)
    def pred_x0(self, sample, model_output, timestep):
        alpha_prod_t = self.scheduler.alphas_cumprod[timestep]

        beta_prod_t = 1 - alpha_prod_t
        if self.scheduler.config.prediction_type == "epsilon":
            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
        elif self.scheduler.config.prediction_type == "sample":
            pred_original_sample = model_output
        elif self.scheduler.config.prediction_type == "v_prediction":
            pred_original_sample = (alpha_prod_t ** 0.5) * sample - (beta_prod_t ** 0.5) * model_output
            # predict V
            model_output = (alpha_prod_t ** 0.5) * model_output + (beta_prod_t ** 0.5) * sample
        else:
            raise ValueError(
                f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
                " or `v_prediction`"
            )

        return pred_original_sample

    def pred_epsilon(self, sample, model_output, timestep):
        alpha_prod_t = self.scheduler.alphas_cumprod[timestep]

        beta_prod_t = 1 - alpha_prod_t
        if self.scheduler.config.prediction_type == "epsilon":
            pred_eps = model_output
        elif self.scheduler.config.prediction_type == "sample":
            pred_eps = (sample - (alpha_prod_t ** 0.5) * model_output) / (beta_prod_t ** 0.5)
        elif self.scheduler.config.prediction_type == "v_prediction":
            pred_eps = (beta_prod_t ** 0.5) * sample + (alpha_prod_t ** 0.5) * model_output
        else:
            raise ValueError(
                f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
                " or `v_prediction`"
            )

        return pred_eps

    # Gaussian blur
    def gaussian_blur_2d(self, img, kernel_size, sigma):
        if not hasattr(self, "kernel2d"):
            ksize_half = (kernel_size - 1) * 0.5

            x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)

            pdf = torch.exp(-0.5 * (x / sigma).pow(2))

            x_kernel = pdf / pdf.sum()
            x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)

            kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
            kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
            self.kernel2d = kernel2d
        kernel2d = self.kernel2d
        padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]

        img = F.pad(img, padding, mode="reflect")
        img = F.conv2d(img, kernel2d, groups=img.shape[-3])

        return img